Hospital Volume is a Determinant of Postoperative Complications, Blood Transfusion and Length of Stay After Radical or Partial Nephrectomy
Bibliographic record
Abstract
PURPOSE: We examined the impact of hospital volume on short-term outcomes after nephrectomy for nonmetastatic renal cell carcinoma. MATERIALS AND METHODS: Using the Nationwide Inpatient Sample we identified 48,172 patients with nonmetastatic renal cell carcinoma treated with nephrectomy (1998 to 2007). Postoperative complications, blood transfusions, prolonged length of stay and in-hospital mortality were examined. Stratification was performed according to teaching status, nephrectomy type (partial vs radical nephrectomy) and surgical approach (open vs laparoscopic). Multivariable logistic regression models were fitted. RESULTS: Patients treated at high volume centers were younger and healthier at nephrectomy. High hospital volume predicted lower blood transfusion rates (8.5% vs 9.7% vs 11.8%), postoperative complications (14.4% vs 16.6% vs 17.2%) and shorter length of stay (43.1% vs 49.8% vs 54.0%, all p <0.001). In multivariable analyses stratified according to teaching status, nephrectomy type and surgical approach, high hospital volume was an independent predictor of lower rates of postoperative complications (OR 0.73-0.88), blood transfusions (OR 0.71-0.78) and prolonged length of stay (OR 0.76-0.89, all p <0.001). Exceptions were postoperative complications at nonteaching centers (OR 0.94, p >0.05) and blood transfusions in nephrectomies performed laparoscopically (OR 0.68, p >0.05). CONCLUSIONS: On average, high hospital volume results in more favorable outcomes during hospitalization after nephrectomy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".